Comparision of classifier performances in diagnosing congestive heart failure using heart rate variability

In this study, the performance of different discrimination algorithms in the analysis of heart rate variability that are used in discriminating the patients with congestive heart failure from normal subjects were investigated. Classifier algorithms of linear discriminant analysis, k-nearest neighbors, multilayer perceptron, radial basis functions and support vector machines were examined with different parameter values. As a result, the maximum classification accuracy of 91.56% was achieved by using multilayer perceptron with 11 neurons in hidden layer.

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